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Wavelet neural networks (WNNs) were used to investigate the influence of operational variables in the soda-ethanol pulping of oil palm fronds (viz. NaOH concentration (10-30%), ethanol concentration (15-75%), cooking temperature (150-190 ºC), and time (60-180 min)) on the resulting pulp and paper properties (viz. screened yield, kappa number, tensile index, and tear index). Performance assessments demonstrated the predictive capability of WNNs, in that the experimental results of the dependent variables with error less than 6% were reproduced, while satisfactory R-squared values were obtained. It thus corroborated the good fit of the WNNs model for simulating the soda-ethanol pulping process for oil palm fronds. [ABSTRACT FROM AUTHOR]

Conference organizers often face the following problem: Given a collection of submitted papers, select a subset to be presented at the conference. The bulk of the work often amounts to assembling a pool of reviewers and then sending each submitted paper to several reviewers. We present in this paper a technique for finding a good assignment of papers to reviewers. An important feature of the solution we find is that each paper is sent to at least one reviewer who is "as expert as possible" for that paper. A major component of the problem is modeled as a bottleneck version of a capacitated transshipment problem. [ABSTRACT FROM AUTHOR]

A multi-modal search space can be defined as having multiple attraction basins - each basin has a single local optimum which is reached from all points in that basin when greedy local search is used. Optimization in multi-modal search spaces can then be viewed as a two-phase process. The first phase is exploration in which the most promising attraction basin is identified. The second phase is exploitation in which the best solution (i.e. the local optimum) within the previously identified attraction basin is attained. The goal of thresheld convergence is to improve the performance of search techniques during the first phase of exploration. The effectiveness of thresheld convergence has been demonstrated through applications to existing metaheuristics such as particle swarm optimization and differential evolution, and through the development of novel metaheuristics such as minimum population search and leaders and followers. [ABSTRACT FROM AUTHOR]

The present study originated with the concomitant production of 1.94 IU.mL-1 of pectinase, 0.34 IU.mL-1 xylanase and 0.086 IU.mL-1 cellulase from a newly isolated Bacillus firmus SDB9 in pectin salt media. Plackett-Burman Design (PBD) and Box-Behnken design (BBD) were used for optimization of mass production of pectinase using agro-residues. Statistical optimization of medium enhances the pectinase production to 17.55 IU.mL-1, 17.7% higher activity than that of unoptimized medium. The optimal pectinase activity was found at pH 8.5 and 45?C temperature. The enzyme was alkali-stable over a range of pH 7.0 to 10.0 for 1 h and thermostable at 35 to 70?C for 1h. Out of 12 tested metal cations at 1mM concentration, the enzyme was found to be hindered by the presence of three cations, whereas four cations were reported to augment, and the rest marginally subdued the activity. Pretreatment with 15 IU pectinase and 2.7 IU xylanase per gram of OD pulp reduce kappa number by 7.9%. After bleaching sequence D0-Ep-D1, the increment in pulp brightness was 2.7% with acceptable whiteness level. Pretreatment also led to a reduction in ClO2 consumption by 15% with superior brightness level. The 0.83% increase in pulp viscosity along with 3.2% gain in double fold number indicated the maintenance of relative cellulose content and strength of paper. Minor variations in burst index, tensile index and tear index reflected the conserved properties of the treated pulp. Synergistic application of mixed enzymes preparation produced from raw agro-residue headed by pectinase in preference to xylanase is first time set forth in the present study to help in cost economization and propel the pulp and paper industries towards environmental friendly future. [ABSTRACT FROM AUTHOR]

In this paper we consider a system of linear bars, spherically jointed at their ends. For each bar one end is linked to the origin. We discuss the equations from which one obtains the deviation of the origin, and some possible optimizations concerning the minimum displacement of the origin and the minimum force in one bar, which are the main goals of the paper. The optimization is performed considering that for two bars one end is unknown; that is, the angles between the bars and the axes are unknown. It is proved that it is difficult to obtain an analytical solution in the general case, and the problem can be discussed only by numerical methods. A numerical case is also studied and some comments concerning the results are given. [ABSTRACT FROM AUTHOR]

Over the last few decades, researches related to the impact of production and industrial manufacturing on environmental sustainability, coupled with multi-dimensional multi-criteria decision analysis and performance evaluation has consistently gained importance. However, no significant work in which an effort to optimize the total production process with the consideration of the positive and negative impacts of each input and output on Environmental sustainability has been noticed. The main aim of our paper is to address this gap. In this paper, an effort has been made to develop a realistic optimization model wherein a manufacturing firm has been considered to function with several decision-making units taking into account the effects of factors like profit, employment generation, social development, environmental pollution and sustainability. The various inputs employed and outputs produced by the firm are responsible for having negative impacts on the environment, besides creative value generation. With due consideration to both these values instead of mere profit, the model determines and explores the firm's decision considering different objectives. The optimization problem has been solved by a GA developed by us and demonstrated with numerical examples. The solutions of the numerical examples point towards some interesting revelations and indicate some prospective future research directions which contributes to this field of research. [ABSTRACT FROM AUTHOR]

A Hybrid Photovoltaic Thermal (PVT) system is one of the most emerging and energy-efficient technologies in the area of solar energy engineering. This review paper provides a comprehensive review of hybrid PVT systems in the context of the history of PVT, general classification, and parameter analysis. Several cell technologies with spectrum analysis are discussed to understand the application's ability and energy efficiency. Hybrid PVT concept, characteristics, and structure analysis is also discussed in this study. An extensive analysis on the classifications of hybrid PVT systems from the recent literature is also presented here. These literatures are identified based on several criteria. In order to provide a complete and energy-efficient technology, an innovative classification of the hybrid PVT system is proposed in this paper. This proposed classification is a combination and upgrade of various existing classifications mentioned in recent research studies. Parameters have a significant and unavoidable impact on the performance and efficiency of the hybrid PVT system. A brief analysis of different parameters and the optimization of the system is conducted after reviewing recent research articles. This analysis provides insights into the impact of parameter variations on the system. A novel parameter model comprising parametric and optimistic analyses is also presented in this paper. It provides a detailed parametric description that significantly affects the performance and efficiency of the hybrid PVT system. Finally, the assessment focuses on a critical analysis of the main challenges in adopting PVT technology and suggests ways to overcome these barriers. [ABSTRACT FROM AUTHOR]

Despite the significance of resource levelling, project managers lack various ways to smooth resource usage fluctuation of a repetitive construction project besides changing resource usage. Tolerating interruptions is an effective way to provide flexibility for a schedule but is ignored when solving resource levelling problems. Therefore, this paper investigates the impacts of interruptions on resource usage fluctuation and develops an integrated approach that simultaneously integrates two scheduling adjusting processes: changing resource usage and tolerating interruptions. In this paper, two interruption conditions are proposed to identify which activities are suitable to be interrupted for smoothing resource usage fluctuation. The traditional resource levelling model is modified to a new scheduling model by incorporating interruptions. A two-stage GA-based scheduling algorithm is developed by integrating changing resource usage and tolerating interruptions. A commonly used pipeline project is adopted to illustrate the steps of the proposed approach and demonstrate its effectiveness and superiority through comparison with previous studies. A large-scale project further verifies the usability of the proposed approach. The results confirmed the feasibility to smooth resource usage fluctuation by interruptions, and the integrated approach can achieve a more competitive resource levelling result. [ABSTRACT FROM AUTHOR]

This paper studies transmission strategies for the downlink of a cloud radio access network, in which the base stations are connected to a centralized cloud computing-based processor with digital fronthaul or backhaul links. We provide a system-level performance comparison of two fundamentally different strategies, namely, the data-sharing strategy and the compression strategy, which differ in the way the fronthaul/backhaul is utilized. It is observed that the performance of both strategies depends crucially on the available fronthaul or backhaul capacity. When the fronthaul/backhaul capacity is low, the data-sharing strategy performs better, while under moderate-to-high fronthaul/backhaul capacity, the compression strategy is superior. Using insights from such a comparison, we propose a novel hybrid strategy, combining the data-sharing and compression strategies, which allows for better control over the fronthaul/backhaul capacity utilization. An optimization framework for the hybrid strategy is proposed. Numerical evidence demonstrates the performance gain of the hybrid strategy. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) techniques have emerged as powerful tools for optimizing energy systems, offering the potential to enhance efficiency, reliability, and sustainability. This review paper provides a comprehensive examination of the applications of RL in the field of energy system optimization, spanning various domains such as energy management, grid control, and renewable energy integration. Beginning with an overview of RL fundamentals, the paper explores recent advancements in RL algorithms and their adaptation to address the unique challenges of energy system optimization. Case studies and real-world applications demonstrate the efficacy of RL-based approaches in improving energy efficiency, reducing costs, and mitigating environmental impacts. Furthermore, the paper discusses future directions and challenges, including scalability, interpretability, and integration with domain knowledge. By synthesizing the latest research findings and identifying key areas for further investigation, this paper aims to inform and inspire future research endeavors in the intersection of reinforcement learning and energy system optimization. [ABSTRACT FROM AUTHOR]

Integrated energy systems (IESs) considering power-to-gas (PtG) technology are an encouraging approach to improve the efficiency, reliability, and elasticity of the system. As the evolution towards decarbonization is increasing, the unified coordination between IESs and PtG technology is also increasing. PtG technology is an option for long-term energy storage in the form of gas, but, compared to other technologies, it is economically expensive at the present time to optimize the technology. This article presents a comprehensive review of the state-of-the-art research and of the developments regarding integrated energy systems considering PtG technology. This presented review emphasizes planning and economic analysis, including system integration enhancements focusing on optimization, conversion technologies, and energy storage to improve the operation and stability and to enhance the facilities for consumers. The role of a PtG system in generation, transmission, distribution, and consumption is discussed. By emphasizing planning, integration, and the role, this paper aims to guide researchers, scientists, engineers, and policy makers towards effective research and broad strategies that sustain an IES-PtG. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) imaging plays a crucial role in various medical applications, but noise in projection data can significantly degrade image quality and hinder diagnosis accuracy. Iterative algorithms for tomographic image reconstruction outperform transform methods, especially in scenarios with severe noise in projections. In this paper, we propose a method to dynamically adjust two parameters included in the iterative rules during the reconstruction process. The algorithm, named the parameter-extended expectation-maximization based on power divergence (PXEM), aims to minimize the weighted extended power divergence between the measured and forward projections at each iteration. Our numerical and physical experiments showed that PXEM surpassed conventional methods such as maximum-likelihood expectation-maximization (MLEM), particularly in noisy scenarios. PXEM combines the noise suppression capabilities of power divergence-based expectation-maximization with static parameters at every iteration and the edge preservation properties of MLEM. The experimental results demonstrated significant improvements in image quality in metrics such as the structural similarity index measure and peak signal-to-noise ratio. PXEM improves CT image reconstruction quality under high noise conditions through enhanced optimization techniques. [ABSTRACT FROM AUTHOR]

Renewable energy sources such as solar are becoming increasingly popular worldwide. Mathematical derivation is used to show the structural framework of PV cells and their models with a single-diode configuration. This paper proposed a two-step optimization technique for extracting the unknown parameters of solar PV cells and modules. The implementation of the proposed techniques is to find the unknown parameters of the PV module from Kyocera (KC200GT). First, we configured the single-diode configuration of the PV equation in terms of five unknown parameters ( I ph , I 0 , V t , R s , and R sh ) and in terms of two unknown parameters ( R s and V t ). After that, we implemented the proposed two-step optimization techniques for extracting the unknown parameters ( I ph , I 0 , V t , R s , and R sh ) of the PV module Kyocera (KC200GT). Also, we used the genetic algorithm (GA) and particle swarm optimization (PSO) techniques to find the unknown parameters ( I ph , I 0 , V t , R s , and R sh ) of the PV module from Kyocera (KC200GT). The performance of the proposed two-step optimization techniques was compared with the traditional single-stage optimization techniques: particle swarm optimization (PSO), genetic algorithms (GAs), grey wolf optimization (GWO), Villalva's method, Accarino's method, Iterative method, and Silva's method. The test results and the output P–V waveform indicate that the proposed method is more efficient and has a greater impact than the standard techniques. [ABSTRACT FROM AUTHOR]

The present paper provides a comparison of different optimization techniques applied to the model-based design of a Buck Zero Voltage Switching (ZVS) Quasi-Resonant DC-DC Converter. The comparison was made both on the basis of the duration of the optimization procedures and in terms of guaranteeing the performance of the power electronic device. The main task of the paper is to present various techniques based on the use of mathematical software for the optimal design of Quasi-Resonant DC-DC converters. These topologies were chosen because in them, the design is carried out according to computational procedures, in which several iterations are often necessary for the successful completion of the process. An optimization procedure with a target function reference curve of the output voltage was used. In this way, the optimization is performed without the need for a complete design of the device but only by using base ratios, design constraints, and past experience to determine initial values and intervals of change in circuit parameters. This is also the main advantage of the used optimization of the reference curve type of the output, compared to applying other objective functions, such as achieving minimum losses or maximum efficiency of the device. [ABSTRACT FROM AUTHOR]

Micro-irrigation pipe network systems are commonly utilized for water transmission and distribution in agricultural irrigation. They effectively transport and distribute water to crops, aiming to achieve water and energy conservation, increased yield, and improved quality. This paper presents a model for the scaled micro-irrigation pipeline network system and provides a comprehensive review of the fundamental concepts and practical applications of optimization techniques in the field of pipeline network design. This paper is divided into four main sections: Firstly, it covers the background and theoretical foundations of optimal design for scaled micro-irrigation pipeline network systems. Secondly, the paper presents an optimal design model specifically tailored for scaled micro-irrigation pipeline networks. And then, it discusses various optimization solution techniques employed for addressing the design challenges of scaled micro-irrigation pipeline networks, along with real-world case studies. Finally, this paper concludes with an outlook on the ongoing research and development efforts in the field of scaled micro-irrigation pipeline network systems. In addition, this paper establishes a fundamental model for optimizing pipeline networks, to achieve minimum safe operation and total cost reduction. It considers constraints such as pipeline pressure-bearing capacity, maximum flow rate, and diameter. The decision-making variables include pipeline diameter, length, internal roughness, node pressure, future demand, and valve placement. Additionally, this paper provides an extensive overview of deterministic methods and heuristic algorithms utilized in the optimal design of micro-irrigation pipeline networks. Finally, this paper presents future research directions for pipeline network optimization and explores the potential for algorithmic improvements, integration of machine learning techniques, and wider adoption of EPANET 2.0 software. These endeavors aim to lay a strong foundation for effectively solving complex and challenging optimization problems in micro-irrigation pipeline network systems in the future. [ABSTRACT FROM AUTHOR]

Recently, the machine learning research trend expands to the system performance optimization field, where it has still been proposed by researchers based on their intuitions and heuristics. Compared to conventional major machine learning research areas such as image or speech recognition, machine learning-based system performance optimization fields are at the beginning stage. However, recent papers show that this approach is promising and has significant potential. This paper reviews 11 machine learning-based system performance optimization approaches from nine recent papers based on well-known machine learning models such as perceptron, LSTM, and RNN. This survey provides a detailed design and summarizes model, input, output, and prediction method of each approach. This paper covers various system performance areas from the data structure to essential system components of a computer system such as index structure, branch predictor, sort, and cache management. The result shows that machine learning-based system performance optimization has an important potential for future research. We expect that this paper shows a wide range of applicability of machine learning technology and provides a new perspective for system performance optimization. [ABSTRACT FROM AUTHOR]

Owing to increasing competition, the marketing of commercial software products is a challenging task. Diverse marketing strategies are adopted to tap the huge market share. Among these, pricing and warranty are the most significant ones. The warranty policies organizations adopt usually have two dimensions: duration and effort spent during the warranty phase. In this paper, two variables are broadly taken: testing, which is an essential phase during software development, and warranty, which is a crucial component of after‐sales service. Here, we will formulate an optimization problem where returns and expenses will be influenced by price, testing resources spent, the duration for which testing is done, warranty duration and effort spent during warranty for the software product. The product sales are assumed to follow a Stochastic Bass model based on Non‐Homogeneous Poisson Process (NHPP). A free on‐site support warranty with an upper limit on duration and effort covers the 2D failure process. The numerical illustration is presented wherein profit is maximized by conjointly optimizing five variables, that is price, testing duration, testing effort, warranty duration and warranty effort. This study emphasizes on application of a popular soft computing, metaheuristic technique named genetic algorithm (GA) to solve an optimization problem for a software system. The solution to the Optimization problem is obtained using the GA tool in MATLAB. The results obtained have relevance in the industry for finding optimal values of significant variables before the release of newly developed software products to the ultimate client. The concept discussed in this paper has diverse applications in computer engineering, software and hardware industry. [ABSTRACT FROM AUTHOR]

This paper provides a mathematical optimization strategy for optimal municipal solid waste management in the context of the COVID-19 epidemic. This strategy integrates two approaches: optimization and machine learning models. First, the optimization model determines the optimal supply chain for the municipal waste management system. Then, machine learning prediction models estimate the required parameters over time, which helps generate future projections for the proposed strategy. The optimization model was coded in the General Algebraic Modeling System, while the prediction model was coded in the Python programming environment. A case study of New York City was addressed to evaluate the proposed strategy, which includes extensive socioeconomic data sets to train the machine learning model. We found the predicted waste collection over time based on the socioeconomic data. The results show trade-offs between the economic (profit) and environmental (waste sent to landfill) objectives for future scenarios, which can be helpful for possible pandemic scenarios in the following years. [ABSTRACT FROM AUTHOR]

Improvement of the energy conversion processes efficiency helps to achieve a more reliable energy supply, a cleaner environment, more competitive businesses, and higher living standard. Industry data indicate significant potential for improving the efficiency of steam systems and minimizing their operating costs by implementing various measures. The present work is a result of a systematic approach for energy performance analysis and identification of opportunities for optimizing the steam-condensate system of the combined heat and power plant ESM Energetika, Skopje, North Macedonia. The boiler plants provide superheated steam used in a hot-water station for the district heating system, for electricity generation, and as process steam for industrial customers. As the main operating costs of the plant stem from the natural gas consumption, the implementation of a set of energy efficiency measures will lead to its reduction, accompanied by less environmental impact. As a result of the system analysis, a number of energy efficiency measures have been identified. For each measure, the impact on individual parts of the system, as well as on the system as a whole, is evaluated using the steam system modeller tool. This paper elaborates some of the identified measures that are considered more reliable from an operational and financial aspect, mainly focused on steam production for the district heating system. Based on a conservative approach, significant potential for savings of natural gas, electrical energy, and treated water is estimated, which will lead to annual financial savings of about 245000 Euro. [ABSTRACT FROM AUTHOR]

Stochastic simulation optimization has been proposed by several researchers to optimize construction operations. Traditionally, explicit averaging is used to estimate the objective functions of candidate solutions. This is carried out by calculating the average estimates of the objective functions obtained from a number of simulation replications. However, the computation effort increases as the number of replications and the size of the search space increase. The main objective of this paper is to study the benefits of using implicit averaging and common random numbers to improve the quality of the optimum solutions while reducing the computation time. The initial results of this study showed a 91% reduction in the computation time and 2.6% improvement in the quality of the optimum solutions. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to suppressing bounded exogenous disturbances in a linear discrete-time control system by a static state- or output-feedback control law. The approach is based on reducing the original problem to a nonconvex matrix optimization problem with the gain matrix as one variable. The latter problem is solved by the gradient method; its convergence is theoretically justified for several important special cases. An example is provided to demonstrate the effectiveness of the iterative procedure proposed. [ABSTRACT FROM AUTHOR]

In improving PV system performance, the parameters associated with electrical photovoltaic equivalent models play a pivotal role. However, due to the increased mathematical complexities and non-linear traits of PV cells, the precise prediction of these parameters is a challenging task. To estimate the parameters associated with PV models, a reliable, robust, and accurate optimization technique is needed. This paper introduces a new algorithm, Rat Swarm Optimizer (RSO), for obtaining the optimum PV cell and module parameters. The proposed method maintains an adequate balance between the exploration and exploitation phases to overcome premature particle issues. The results obtained using RSO are compared with those of other algorithms, i.e., Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Salp Swarm Algorithm (SSA), Harris Hawks Optimization (HHO), and Grasshopper Optimization (GOA), in this work. The modified one-diode model (MODM) and modified two-diode model (MTDM) are used to analyze the parameters of the mono-crystalline PV cell using the suggested RSO. The obtained findings imply that the parameters estimated by the suggested RSO are more accurate than those calculated by the other algorithms taken into consideration in the paper. The statistical results are compared, and it is clear that RSO is a very accurate, fast, and dependable approach for the parameter estimation of PV cells. [ABSTRACT FROM AUTHOR]

Few of the challenges with development of a single on–board motor for direct–drive electric vehicles include high torque density and low torque ripple. Therefore, in this paper, a 36–slot, 34–pole consequent pole six–phase permanent magnet synchronous machine (PMSM) has been optimized to address the aforementioned challenges for direct–drive application. Existing literature on optimization processes that rely solely on finite element models are restricted to three–phase machines only and also take longer computation time. Therefore, this paper proposes a novel optimization approach based on supervised machine learning for six–phase PMSM. In this approach, a non–conventional extended dual dq–frame model that accounts for higher order space harmonics in inductances and flux linkages has been developed and used for accurate computation of average torque and torque ripple of six–phase PMSM. Using the performance characteristics obtained from the extended dual dq–frame model for a set of initial design candidates, support vector regression algorithm is employed for supervised machine learning and increasing solutions in the design space. Furthermore, pareto front is used for selecting optimal machine models with maximum torque density and reduced torque ripple. Multi–objective trade–offs and comparison of initial and optimized designs based on average torque, torque ripple, efficiency and cost are performed. [ABSTRACT FROM AUTHOR]

Business process optimization (BPO) has become an increasingly attractive subject in the wider area of business process intelligence and is considered as the problem of composing feasible business process designs with optimal attribute values, such as execution time and cost. Despite the fact that many approaches have produced promising results regarding the enhancement of attribute performance, little has been done to reduce the computational complexity due to the size of the problem. The proposed approach introduces an elaborate preprocessing phase as a component to an established optimization framework (bpoF) that applies evolutionary multi-objective optimization algorithms (EMOAs) to generate a series of diverse optimized business process designs based on specific process requirements. The preprocessing phase follows a systematic rule-based algorithmic procedure for reducing the library size of candidate tasks. The experimental results on synthetic data demonstrate a considerable reduction of the library size and a positive influence on the performance of EMOAs, which is expressed with the generation of an increasing number of nondominated solutions. An important feature of the proposed phase is that the preprocessing effects are explicitly measured before the EMOAs application; thus, the effects on the library reduction size are directly correlated with the improved performance of the EMOAs in terms of average time of execution and nondominated solution generation. The work presented in this paper intends to pave the way for addressing the abiding optimization challenges related to the computational complexity of the search space of the optimization problem by working on the problem specification at an earlier stage. [ABSTRACT FROM AUTHOR]

Electrical Discharge Machining (EDM) is considered as the much traditional, adaptable and unconventional machining process that is proficient in generating complicated structures. Further, it is self-sufficient over the mechanical properties of workpiece materials only if it is conductive. The viability of the EDM process regarding the application types might get considerably improved with the constant enhancement in the personification of numerical control and the metal removal efficacy. This survey intends to make a review of 65 papers related to the optimization and modeling of EDM process machining. The analysis is thus made with regards to the following factors like Al alloy composite in Die Shrinking EDM, Optimization and Al metal matrix composites. Particularly, the hybrid optimization models in various papers are analyzed concerning their logic. Further, the algorithmic and parametric analysis is also made with the contributed papers. Finally, the research gaps and challenges in this EDM processing are discussed, which would pave the way for efficient research works in this area. [ABSTRACT FROM AUTHOR]

Compilation often takes a long time, especially for large projects or when identifying better optimization options. Currently, compilers are mainly installed on local machines and used as standalone software. Despite the availability of several online compilers, they do not offer an efficient all-in-one package for private account management, command line interface (CLI), code advisors, and optimization techniques. Today, the widespread usage of Software as a Service (SaaS) is ever-growing, and compilers are not an exception. In this paper, we describe a symmetric approach to compilation and how to compile code on distributed systems. Although some improvements in cloud compilers have been made, it is possible to harness the potential of the most-modern technologies and architecture patterns toward designing efficient, in-cloud compilers. In this paper, we propose an architecture design of a cloud-based compiler that is fully compatible with orchestration technologies, such as Kubernetes, providing a higher level of scalability, reliability, security, and maintainability. Microservice architecture alongside containerization and orchestration technologies assist us in making a scalable system that provides a high level of availability. We propose this architecture so that the system can handle a higher workload as it receives a large number of compilation requests per second. Distributed compilation is a prominent benefit of this approach, as each phase of the compilation can be executed in a separate server, which supplies a kind of workload mitigation to the whole system. In other words, we propose a new perspective for an intelligent way of advisor, error detection, and optimization of compilers. We also propose an implementation example of the developed architecture. Finally, we analyze the results from an experimental implementation, proving that we can compile code from more than 100k requests concurrently on a cloud cluster with one master node and three worker nodes. [ABSTRACT FROM AUTHOR]

The growth in linked and autonomous vehicles has led to the emergence of vehicular ad hoc networks (VANETs) as a means to enhance road safety, traffic efficiency, and passenger comfort. However, VANETs face challenges in facilitating trustworthiness and high-quality services due to communication delays caused by traffic, dynamic topology changes, variable speeds, and other influencing factors. Hence, there is a need for a reliable data dissemination scheme capable of reducing communication delays among hops by identifying effective forwarder nodes. In this paper, we propose a novel, weighted, estimated, spider monkey-based, nature-inspired optimization (w-SMNO) method to generate a set of efficient relays. Additionally, we introduce a dynamic weight assignment and configuration model to enhance system accuracy using a neural network based on backpropagation with gradient descent optimization techniques to minimize errors in the machine learning model. The w-SMNO also incorporates a distinct algorithm for effective relay selection among multiple monkey spider groups. The simulation results demonstrate substantial improvements in w-SMNO, with a 35.7% increase in coverage, a 41.2% reduction in the end-to-end delay, a 36.4% improvement in the message delivery rate, and a 38.4% decrease in the collision rate compared to the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Many different aspects of software system development and verification rely on precise function identification in binary code. Recognition of the source Assembly functions in embedded systems is one of the fundamental challenges in binary program analysis. While numerous approaches assume that the functions are given a priori, correct identification of the functions in binaries remains a great issue. This contribution addresses the problem of uncertainty in binary code in identification of functions, which were optimized during compilation. This paper investigates the difference between debug and optimized functions via modeling of these functions. To do so, we introduce an extensible model-centred hands-on approach for examining similarities between binary functions. The main idea is to model each function using a set of predetermined, experimentally discovered features, and then find a suitable weight vector that could give impact factor to each such a feature. After finding the weight vector, the introduced models of such desired functions can be identified in binary software packages. It means that we reduce the similarity identification problem of the models to a classical version of optimization problems with one optimization criterion. Using our implementation, we found that the proposed approach works smoothly for functions, which contain at least ten Assembly instructions. Our tool guarantees success at a very high level. [ABSTRACT FROM AUTHOR]

Medium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) measurements serve as valuable tools for assessing the insulation state, complexity arises from the presence of diverse discharge sources, making the evaluation of PD data challenging. The reliability of diagnostics for MV cables hinges on the precise interpretation of PD activity. To streamline the repair and maintenance of cables, it becomes crucial to discern and categorize PD types accurately. This paper presents a comprehensive review encompassing the realms of detection, feature extraction, artificial intelligence, and optimization techniques employed in the classification of PD signals/sources. Its exploration encompasses a variety of sensors utilized for PD detection, data processing methodologies for efficient feature extraction, optimization techniques dedicated to selecting optimal features, and artificial intelligence-based approaches for the classification of PD sources. This synthesized review not only serves as a valuable reference for researchers engaged in the application of methods for PD signal classification but also sheds light on potential avenues for future developments of techniques within the context of MV cables. [ABSTRACT FROM AUTHOR]

Simulation models are a valuable tool for exoskeleton development, especially for system optimization and evaluation. It allows an assessment of the performance and effectiveness of exoskeletons even at an early stage of their development without physical realization. Due to the closed physical interaction between the exoskeleton and the user, accurate modeling of the human–exoskeleton interaction in defined scenarios is essential for exoskeleton simulations. This paper presents a novel approach to simulate exoskeleton motion in response to human motion and the interaction forces at the physical interfaces between the human and the exoskeleton. Our approach uses a multibody model of a shoulder exoskeleton in MATLAB R2021b and imports human motion via virtual markers from a digital human model to simulate human–exoskeleton interaction. To validate the human-motion-based approach, simulated exoskeleton motion and interaction forces are compared with experimental data from a previous lab study. The results demonstrate the feasibility of our approach to simulate human–exoskeleton interaction based on human motion. In addition, the approach is used to optimize the support profile of an exoskeleton, indicating its potential to assist exoskeleton development prior to physical prototyping. [ABSTRACT FROM AUTHOR]

A positively invariant set is an important concept in dynamical systems. The study of positively invariant set conditions for discrete-time systems is one interesting topic in both theoretical studies and practical applications research. Different methods for characterizing the invariance of different types of sets have been established. For example, the ellipsoidal and the Lorenz cone, which are quadratic convex sets, have different properties from a polyhedral set. This paper presents an optimization method and a dual optimization method to characterize the positive invariance of the ellipsoidal and the Lorenz cone. The proposed methods are applicable to both linear and nonlinear discrete-time systems. Using nonlinear programming and an induced norm, the positive invariance condition problems are transformed into optimization problems, and the dual optimization method is also used to give equivalent dual forms. Fewer results on the positive invariance condition of Lorenz cones can be found than for the other type of set; this paper fulfills the results of this problem. In addition, the proposed methods in this paper provide more options for checking the positive invariance of quadratic convex sets from the perspective of optimization and dual optimization. The effectiveness of this method is demonstrated by numerical examples. [ABSTRACT FROM AUTHOR]

As it tackles electrical and non‐electrical losses, the triple‐diode model (TDM) of photovoltaic (PV) cells is highly exact. This paper employs a novel optimization method known as the innovative optimization algorithm (INFO) technique to correctly estimate the electrical characteristics of such TDM. To shift agents towards a better position, the INFO algorithm exploits the concept of weighted mean. The primary goal of INFO is to stress its performance features to solve some optimization difficulties that other approaches cannot effectively solve. In this paper, the objective function based on a combination of the absolute value of the current error, its squared value, and its quadrable value is employed, which the INFO optimizer minimizes to predict the optimum parameters of such TDM precisely. The proposed INFO algorithm is carried out on multi‐ and mono‐crystalline varieties, such as the Kyocera KC200GT and the Canadian Solar CS6K‐280 M. The simulation outcomes demonstrate the INFO's ability to extract the model parameters precisely. The INFO achieved the lowest ideal fitness values of 9.0738 × 10−06 and 5.7356 × 10−05 for the KC200GT and Canadian Solar CS6K‐280 M, respectively, throughout the optimization procedure. Under various environmental circumstances, experimental validation of the calculated parameters using the (INFO) optimizer is carried out, and the results are compared to the observed values from the laboratory experiments. The simulation results demonstrate the INFO's convergence time and accuracy advantage over competing optimization techniques. Additionally, statistical analysis shows that the INFO optimizer is resilient. [ABSTRACT FROM AUTHOR]

By triggering the switches in sequence and controlling the brick charging voltage independently, a pulsed power system can generate flexible current waveform in the load, which could allow high-accurate magnetic pressure in experimental materials in the study of EOS data and dynamic response of material characterization. Currently, a 1.5-MA driver with 20 independent bricks has been developed, and a programmable 256-brick parallel discharge driver which is able to generate shaped current greater than 6.5 MA is under construction in the China Academy of Engineering Physics. In this paper, for these two multibrick parallel discharge drivers, the method of optimizing the output current waveform of the driver is studied by using the genetic algorithm and the corresponding optimization results of different target current waveforms are obtained. An experimental platform is set up for studying the gas switch breakdown characteristics. The self-breakdown and triggering breakdown experiments of the switch are conducted, and its breakdown characteristics are researched. Meanwhile, the effect of breakdown delay and jitter on the output current waveform is studied by simulation. This paper is of great significance to the actual operation of the multibrick parallel discharge drivers. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamic access point (AP) clustering and precoding problem in the downlink of user-centric virtual cell networks. The goal is to maximize the weighted sum spectral efficiency (SE) while satisfying the power constraints and AP clustering constraints in adjacent time slots (TSs). By adopting the random walk mobility to model the mobile user equipments’ movement behaviors, we consider dynamic and time-varying channel conditions. Therefore, the weighted sum SE maximization programming takes the form of discrete-time sequence of mixed-integer non-convex optimization problems. In this paper, we propose to solve this sequential problem in two stages. In the first stage, a dynamic AP clustering approach based on discrete particle swarm optimization is developed. This approach takes the advantage of the channel correlation by exploiting the relationship between AP clustering solutions in adjacent TSs to improve the SE performance and reduce complexity. In the second stage, given the AP clustering solution obtained in the first stage, a distributed precoding algorithm is devised via applying the weighted minimum mean square error method. By combining these two stages, we propose a novel dynamic AP clustering and precoding algorithm (DAPC-Pre). The effectiveness of the proposed DAPC-Pre algorithm is verified by the simulation results. In particular, the proposed algorithm converges fast and significantly outperforms benchmark algorithms in terms of sum SE under different dynamic environments. [ABSTRACT FROM AUTHOR]

This paper treats the problem of optimal resource allocation over time in a finite-horizon setting, in which the resource become available only sequentially and in incremental values, and the utility function is concave and can freely vary over time. Such resource allocation problems have direct applications in data communication networks (e.g., energy harvesting systems). This problem is studied extensively for special choices of the concave utility function (time invariant and logarithmic) in which case the optimal resource allocation policies are well-understood. This paper treats this problem in its general form and analytically characterizes the structure of the optimal resource allocation policy and devises an algorithm for computing the exact solutions analytically. An observation instrumental to devising the provided algorithm is that there exist time instances at which the available resources are exhausted, with no carryover to future. This algorithm identifies all such instances, which in turn, facilitates breaking the original problem into multiple problems with significantly reduced dimensions. Furthermore, some widely used special cases in which the algorithm takes simpler structures are characterized, and the application to the energy harvesting systems is discussed. Numerical evaluations are provided to assess the key properties of the optimal resource allocation structure and to compare the performance with the generic convex optimization algorithms. [ABSTRACT FROM AUTHOR]

In the field of reinforcement learning, we propose a Correct Proximal Policy Optimization (CPPO) algorithm based on the modified penalty factor β and relative entropy in order to solve the robustness and stationarity of traditional algorithms. Firstly, In the process of reinforcement learning, this paper establishes a strategy evaluation mechanism through the policy distribution function. Secondly, the state space function is quantified by introducing entropy, whereby the approximation policy is used to approximate the real policy distribution, and the kernel function estimation and calculation of relative entropy is used to fit the reward function based on complex problem. Finally, through the comparative analysis on the classic test cases, we demonstrated that our proposed algorithm is effective, has a faster convergence speed and better performance than the traditional PPO algorithm, and the measure of the relative entropy can show the differences. In addition, it can more efficiently use the information of complex environment to learn policies. At the same time, not only can our paper explain the rationality of the policy distribution theory, the proposed framework can also balance between iteration steps, computational complexity and convergence speed, and we also introduced an effective measure of performance using the relative entropy concept. [ABSTRACT FROM AUTHOR]

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This paper proposes an innovative image compression scheme by utilizing the Adaptive Discrete Wavelet Transform-based Lifting Scheme (ADWT-LS). The most important feature of the proposed DWT lifting method is splitting the low-pass and high-pass filters into upper and lower triangular matrices. It also converts the filter execution into banded matrix multiplications with an innovative lifting factorization presented with fine-tuned parameters. Further, optimal tuning is the most important contribution that is achieved via a new hybrid algorithm known as Lioness-Integrated Whale Optimization Algorithm (LI-WOA). The proposed algorithm hybridizes the concepts of both the Lion Algorithm (LA) and Whale Optimization Algorithm (WOA). In addition, innovative cosine evaluation is initiated in this work under the CORDIC algorithm. Also, this paper defines a single objective function that relates multi-constraints like the Peak Signal-to-Noise Ratio (PSNR) as well as Compression Ratio (CR). Finally, the performance of the proposed work is compared over other conventional models regarding certain performance measures. [ABSTRACT FROM AUTHOR]

This paper considers the problem of user association to small-cell base stations (SBSs) in a heterogeneous and small-cell network (HetSNet). Two optimization problems are investigated, namely, maximizing the set of associated users to the SBSs (the unweighted problem) and maximizing the set of weighted associated users to the SBSs (the weighted problem), under signal-to-interference-plus-noise ratio constraints. Both problems are formulated as linear integer programs. The weighted problem is known to be NP-hard, and in this paper, the unweighted problem is proved to be NP-hard as well. Therefore, this paper develops two heuristic polynomial-time algorithms to solve both problems. The computational complexity of the proposed algorithms is evaluated and is shown to be far more efficient than the complexity of the optimal brute-force (BF) algorithm. Moreover, this paper benchmarks the performance of the proposed algorithms against the BF algorithm, the branch-and-bound CPLEX-based algorithm, and state-of-the-art algorithms, through numerical simulations. The results demonstrate the close-to-optimal performance of the proposed algorithms. They also show that the weighted problem can be solved to provide solutions that are fair between users or to balance the load among SBSs. [ABSTRACT FROM PUBLISHER]

Total variation (TV) is a one of the most popular regularizers for stabilizing the solution of ill-posed inverse problems. This paper proposes a novel proximal-gradient algorithm for minimizing TV regularized least-squares cost functionals. Unlike traditional methods that require nested iterations for computing the proximal step of TV, our algorithm approximates the latter with several simple proximals that have closed form solutions. We theoretically prove that the proposed parallel proximal method achieves the TV solution with arbitrarily high precision at a global rate of converge that is equivalent to the fast proximal-gradient methods. The results in this paper have the potential to enhance the applicability of TV for solving very large-scale imaging inverse problems. [ABSTRACT FROM AUTHOR]

The emergence of interference alignment (IA) as a degrees-of-freedom optimal strategy motivates the need to investigate whether IA can be leveraged to aid conventional network utility maximization algorithms, which are typically only capable of finding locally optimal solutions. To test the usefulness of IA in this context, this paper proposes a two-stage optimization framework for the downlink of a $G$ -cell multi-antenna network with $K$ users/cell. The first stage of the proposed framework focuses on nulling interference from a set of dominant interferers using IA, while the second stage optimizes transmit and receive beamformers to maximize a network-wide utility using the IA solution as the initial condition. Further, this paper establishes a set of new feasibility results for partial IA that can be used to guide the number of dominant interferers to be nulled in the first stage. Through simulations on specific topologies of a cluster of base stations, it is observed that the impact of IA depends on the choice of the utility function and the presence of out-of-cluster interference. In the absence of out-of-cluster interference, the proposed framework outperforms straightforward optimization when maximizing the minimum rate, while providing marginal gains when maximizing sum rate. However, the benefit of IA is greatly diminished in the presence of significant out-of-cluster interference. [ABSTRACT FROM AUTHOR]

The eminence of Economic Dispatch (ED) in power systems is significantly high as it involves in scheduling the available power from various power plants with less cost by compensating equality and inequality constrictions. The emission of toxic gases from power plants leads to environmental imbalance and so it is highly mandatory to rectify this issues for obtaining optimal performance in the power systems. In this present study, the Economic and Emission Dispatch (EED) problems are resolved as multi objective Economic Dispatch problems by using Harris Hawk's Optimization (HHO), which is capable enough to resolve the concerned issue in a wider range. In addition, the clustering approach is employed to maintain the size of the Pareto Optimal (PO) set during each iteration and fuzzy based approach is employed to extricate compromise solution from the Pareto front. To meet the equality constraint effectively, a new demand-based constraint handling mechanism is adopted. This paper also includes Wind energy conversion system (WECS) in EED problem. The conventional thermal generator cost is taken into account while considering the overall cost functions of wind energy like overestimated, underestimated and proportional costs. The quality of the non-dominated solution set is measured using quality metrics such as Set Spacing (SP) and Hyper-Volume (HV) and the solutions are compared with other conventional algorithms to prove its efficiency. The present study is validated with the outcomes of various literature papers. [ABSTRACT FROM AUTHOR]

Clean and renewable energy is the only way to achieve sustainable energy development, with considerable social and economic benefits. As a key technology for clean and renewable energy, it is very important to research the reliability optimization of microgrids. This paper reviews the research progress in microgrid reliability optimization. This paper first classifies and summarizes the existing research on microgrid control strategies and reliability assessment. Then, the system reliability optimization framework is summarized in terms of both microgrid systems and optimization objectives. Next, we summarize the most commonly used optimization algorithms for microgrid reliability for different microgrid systems. Finally, we provide a bibliometric analysis of the literature on the reliability research of microgrids. In addition, we propose some research challenges in the future for the reliability of microgrids. [ABSTRACT FROM AUTHOR]

As the semiconductor manufacturing process is moving towards the 3 nm node, there is a crucial need to reduce the edge placement error (EPE) to ensure proper functioning of the integrated circuit (IC) devices. EPE is the most important metric that quantify the fidelity of fabricated patterns in multi-patterning processes, and it is the combination of overlay errors and critical dimension (CD) errors. Recent advances in machine learning have enabled many new possibilities to improve the performance and efficiency of EPE optimization techniques. In this paper, we conducted a survey of recent research work that applied machine learning/ deep learning techniques for the purposes of enhancing virtual overlay metrology, reducing overlay error, and improving mask optimization methods for EPE reduction. Thorough discussions about the objectives, datasets, input features, models, key findings, and limitations are provided. In general, the results of the review work show a great potential of machine learning techniques in aiding the improvement of EPE in the field of semiconductor manufacturing. [ABSTRACT FROM AUTHOR]

Wildfires are recurrent natural events that have been increasing in frequency and severity in recent decades. They threaten human lives and damage ecosystems and infrastructure, leading to high recovery costs. To address the issue of wildfires, several activities must be managed and coordinated in order to develop a suitable response that is both effective and affordable. This includes actions taken before (mitigation, prevention, and preparedness), during (response), and after the event (recovery). Considering the available resources and the safety of the involved personnel is a key aspect. This article is a review focused on fire suppression, which comprises actions belonging to the preparedness phase (deployment) and the response phase (dispatching) of the wildfire management scheme. It goes through the models and methodologies that, applying operations research and optimization techniques, address the management of resources to address fire suppression. This article presents a review of the studies published after the last review on the topic in 2017, but also includes some interesting papers before that date. It concludes with some classifying tables and a few conclusions about possible future lines of research. [ABSTRACT FROM AUTHOR]

Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple complex constraints. Solving such optimization problems requires sophisticated algorithms and optimization techniques. A major trend in recent years is the use of nature-inspired metaheustic algorithms (NIMA). Despite the popularity of nature-inspired metaheuristic algorithms, there are still some challenging issues and open problems to be resolved. Two main issues related to current NIMAs are: there are over 540 algorithms in the literature, and there is no unified framework to understand the search mechanisms of different algorithms. Therefore, this paper attempts to analyse some similarities and differences among different algorithms and then presents a generalized evolutionary metaheuristic (GEM) in an attempt to unify some of the existing algorithms. After a brief discussion of some insights into nature-inspired algorithms and some open problems, we propose a generalized evolutionary metaheuristic algorithm to unify more than 20 different algorithms so as to understand their main steps and search mechanisms. We then test the unified GEM using 15 test benchmarks to validate its performance. Finally, further research topics are briefly discussed. [ABSTRACT FROM AUTHOR]

Reactive distillation (RD) combines chemical reactions and separation in a single unit essential to equilibrium-limited reactions. This new technique encompasses multiple advantages over traditional processes, including lower operating costs, increased thermal energy efficiency, high product selectivity, high purity percentage, and lower environmental impact. This paper provided an overview of the features, industrial applications, and industrial perspective of advanced reactive distillation technologies (ARDTs). This study focused on five under-development ARDTs: reactive dividing wall column (R-DWC), reactive high-gravity distillation (R-HiGee), reactive heat-integrated distillation column (R-HIDiC), catalytic cyclic distillation (CCD), and membrane-assisted reactive distillation (MA-RD). The primary drivers for new RD applications are reduced number of vessels, reduced residence time and holdup volume, increased mass and heat transfer, overcoming azeotropes, and prefractionation or impurity removal. ARDT's potential has yet to be studied, and research remains active to improve it further by investigating other RD technologies, simulation, and optimization techniques. [ABSTRACT FROM AUTHOR]